Data cleaning - Remove the outliers / check feasibilty

[Stefpur]

Description:

The numerical values in the columns Goal, Pledged, and Backers appear to be highly skewed, which may indicate the presence of outliers.

Task:
check and address this

Visualize the distribution of the data:
Create histograms and boxplots to check the distribution of the data and identify outliers.

Remove outliers:
Apply a method to remove outliers, such as setting a threshold or using the Interquartile Range (IQR).

Delete outliers (z.B. mit IQR-Methode)
def remove_outliers(df, column):
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]

for feature in numerical_features:
kickstarter = remove_outliers(kickstarter, feature)

If evaluation is positive, a function will be created and added to the base.jpyt

[Essejran]

New ticket: deciding on best transformation for outliers